增加queueing对接
parent
6e66ccffd6
commit
26ea7e395d
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@ -1,2 +1,3 @@
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/venv/
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/__pycache__/
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/__pycache__/
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/logs
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{
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"abp": {
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"api_base": "https://stag-abp-api.iconsz.com",
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"tenant": "ics",
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"username": "ics",
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"password": "1qaz@WSX",
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"client_id": "engine_InfoExtractor",
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"client_secret": "7ii7U9AabN2*3000",
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"scope": "FX"
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},
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"engine": {
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},
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"queue_processor": {
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"get_pending_queue_interval_seconds": 2
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},
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"queue_client": {
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"check_queue_interval_seconds": 1,
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"call_wait_return_default_timeout_seconds": 60
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}
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}
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@ -1,3 +1,4 @@
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API_KEY = 'ollama'
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MODEL = 'qwen2.5:32b-instruct-q5_K_M'
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LLM_BASE_URL = 'http://localhost:11434/v1'
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API_KEY = "ollama"
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MODEL = "qwen2.5:32b-instruct-q5_K_M"
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# MODEL = "qwen2.5:7b"
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LLM_BASE_URL = "http://localhost:11434/v1"
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from dotenv import load_dotenv
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load_dotenv()
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import time
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from openai import OpenAI
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from datetime import datetime
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import config, debug, prompt, json
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"""
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备注：
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目前使用ollama部署的qwen2.5:32b-instruct-q5_K_M,
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如果输入字符串过长(大致超过1000个字符或1500token)会因上下文窗口过短导致提示词被遗忘，
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进而输出不合规定的结果！
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"""
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llm = OpenAI(base_url=config.LLM_BASE_URL, api_key=config.API_KEY)
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class entityExtractionProcess:
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def entity_extract(input_text):
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messages = [
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{"role": "system", "content": prompt.ENTITY_EXTRACT},
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{"role": "system", "content": f"今天的日期是：{str(datetime.today())}"},
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{"role": "user", "content": f"{input_text}"},
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]
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timenow = time.time()
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response = (
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llm.chat.completions.create(
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model=config.MODEL, messages=messages, temperature=0, max_tokens=128_000
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)
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.choices[0]
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.message.content
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)
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print(f"本次输出花费时间：{time.time() - timenow} 秒")
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return json.loads(response)
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git clone https://gitea.iconsz.com/iCON/iCloudEngine.git
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pause
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95
main.py
95
main.py
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@ -1,70 +1,57 @@
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from dotenv import load_dotenv
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load_dotenv()
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import time
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from openai import OpenAI
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from datetime import datetime
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import config, debug, prompt
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from entity_extraction_process import entityExtractionProcess
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import os, sys, time, json, traceback
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from utils.logger import logger
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DEBUG = True
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'''
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备注：
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目前使用ollama部署的qwen2.5:32b-instruct-q5_K_M,
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如果输入字符串过长(大致超过1000个字符或1500token)会因上下文窗口过短导致提示词被遗忘，
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进而输出不合规定的结果！
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'''
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base_dir = os.path.dirname(os.path.abspath(__file__))
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relative_path = os.path.join(base_dir, "iCloudEngine/src")
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sys.path.append(relative_path)
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from queue_processor import QueueProcessor
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llm = OpenAI(
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base_url=config.LLM_BASE_URL,
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api_key=config.API_KEY
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)
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DEBUG = False
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def get_task() -> str:
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# 从引擎中获取任务的接口
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# 返回的类型应为字符串或None
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raise NotImplementedError
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def push_result(result):
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# 将提取结果返回给引擎的接口
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raise NotImplementedError
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class Main(QueueProcessor):
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def processor_handle(self, input):
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print("input:", input)
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currentEngineId = input["currentEngineId"]
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inputData = json.loads(input["inputData"])
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match currentEngineId:
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case 3000: # InfoExtractor 实体信息提取
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input_text = inputData["text"]
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return entityExtractionProcess.entity_extract(input_text)
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def entity_extract(input_text):
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messages = [
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{"role": "system", "content": prompt.ENTITY_EXTRACT},
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{"role": "system", "content": f"今天的日期是：{str(datetime.today())}"},
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{"role": "user", "content": f"{input_text}"}
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]
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timenow = time.time()
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response = llm.chat.completions.create(
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model=config.MODEL,
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messages=messages,
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temperature=0,
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max_tokens=128_000
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).choices[0].message.content
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print(f"本次输出花费时间：{time.time() - timenow} 秒")
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return response
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def run():
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if DEBUG:
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input_text = debug.input_text
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else:
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input_text = get_task()
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if not input_text:
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return
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response = entity_extract(input_text)
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if DEBUG:
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print(response)
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else:
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push_result(response)
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if __name__ == "__main__":
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if DEBUG:
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run()
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input_text = debug.input_text
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response = entityExtractionProcess.entity_extract(input_text)
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print(response)
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exit()
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while True:
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run()
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time.sleep(5)
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else:
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logger.warning("application start")
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try:
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Main().startV2()
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except Exception as e:
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logger.warning(f"excute exception:{e}")
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exc_type, exc_value, exc_traceback = sys.exc_info()
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traceback_details = traceback.extract_tb(exc_traceback)
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filename, line_number, function_name, text = traceback_details[-1]
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logger.error(f"Exception occurred in {filename} at line {line_number}: {e}")
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logger.error(f"Function name: {function_name}")
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logger.error(f"Text: {text}")
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import traceback
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logger.warning(f"Crawler_NewsCN {traceback.format_exc()}")
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finally:
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logger.warning("application completed")
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import os, sys
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base_dir = os.path.dirname(os.path.abspath(__file__))
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relative_path = os.path.join(base_dir, "iCloudEngine/src")
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sys.path.append(relative_path)
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from queue_client import QueueClient
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class submit_test:
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def submit(code, data):
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client = QueueClient()
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returnData = client.call(code, data)
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print(returnData)
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# # 创建一个测试队列：3000 / 实体关系提取。输入一段话，提取人物姓名、性别、出生日期、年龄、职位等信息
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submit_test.submit(
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3000,
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{
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"text": "范九伦简历\n\n范九伦，男，汉族，1964年11月生，研究生，民盟盟员、中共党员，现任民盟中央常委、陕西省委会主委，省政协副主席，西安邮电大学校长"
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},
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)
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import logging
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import os
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from datetime import datetime
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import requests
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if not os.path.exists("logs"):
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os.makedirs("logs")
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# 配置日志的基本设置
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logging.basicConfig(
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filename=os.path.join("logs", datetime.now().strftime("%Y-%m-%d") + ".log"),
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level=logging.WARNING,
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format="%(asctime)s:%(levelname)s:%(message)s",
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filemode="a",
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encoding="utf-8",
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)
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# 设置requests库的日志级别为WARNING或更高，以确保不记录INFO或DEBUG级别的日志
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logging.getLogger("requests").setLevel(logging.WARNING)
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logging.getLogger("urllib3").setLevel(logging.WARNING)
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logging.getLogger("elasticsearch").setLevel(logging.WARNING)
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class logger:
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# def debug(msg: str):
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# logging.debug(msg)
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# def info(msg: str):
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# logging.info(msg)
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def warning(msg, printLog=True):
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logging.warning(msg)
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if printLog == True:
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print(msg)
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def error(msg: str, printLog=True):
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logging.error(msg)
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if printLog == True:
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print(msg)
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# def critical(msg: str):
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# logging.critical(msg)
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